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            <a class="title-main" href="Blog/public">OffSummer</a>
            
            <span class="title-sub">Summer is going, but autumn does not come yet.</span>
            
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<div class="post">
    <h2 class="post-item post-title">
        <a href="Blog/public/post/03-module/">深度学习框架</a>
    </h2>
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        <span><i class="iconfont icon-today-sharp"></i>&nbsp;2022-10-15</span>

<span><i class="iconfont icon-file-tray-sharp"></i>&nbsp;1663 字</span>
<span><i class="iconfont icon-time-sharp"></i>&nbsp;4 分钟</span>
<span><i class="iconfont icon-pricetags-sharp"></i>&nbsp;<a href="/Blog/public/tags/mindspore">mindspore</a>&nbsp;<a href="/Blog/public/tags/dl">DL</a>&nbsp;</span>

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    <div class="post-item post-summary markdown-body">
        <h2 id="回顾">回顾</h2>
<p>首先，我们来回顾一下前两节我们做了什么。</p>
<p>在第一节，我们建立了一个线性数据集，将其打包进行批量计算；通过加权求和的方式，建立了一个线性网络；使用平方误差作为损失函数；通过梯度下降的方式进行反向传播，使线性网络收敛。
在第二节，我们使用Mnist数据集，将其打包进行批量计算；使用了Softmax非线性网络；使用交叉熵作为损失函数；通过梯度下降的方式进行反向传播，使非线性网络收敛。</p>
<p>可以注意到，在两种方式中，我们的操作都有共同的成分，比如打开数据集、设置网络格式、选择误差函数、选择网络训练方法等。不可忽视的是，在前两节中，我们都采用手动求导的方法，进行梯度下降的操作，需要用户熟练掌握微积分。</p>
<p>但实际上，哪怕不是数学家或者信息学家，也能很好地使用深度学习，因为这些东西，深度学习框架都会自动帮我们处理，很多时候，我们需要做的，仅仅只是数据标注，以及超参数的调整而已，哪怕我们对模型的细节一无所知，也可以将其作为一个工具进行使用，这也是调参侠的由来。</p>
<p>下面，我们依照第二节，来了解一下MindSpore是如何为我们实现模型的训练的。</p>
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    <h2 class="post-item post-title">
        <a href="Blog/public/post/02-softmax/">Softmax回归</a>
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        <span><i class="iconfont icon-today-sharp"></i>&nbsp;2022-10-15</span>

<span><i class="iconfont icon-file-tray-sharp"></i>&nbsp;2202 字</span>
<span><i class="iconfont icon-time-sharp"></i>&nbsp;5 分钟</span>
<span><i class="iconfont icon-pricetags-sharp"></i>&nbsp;<a href="/Blog/public/tags/mindspore">mindspore</a>&nbsp;<a href="/Blog/public/tags/dl">DL</a>&nbsp;</span>

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        <h2 id="softmax">Softmax</h2>
<p>我们都知道概率是分布在0到1之间，但在第一节中，在不限定作用域的情况下，线性函数的值域分布在无穷的区间内，那么有没有一个函数能够压缩其值域到0~1之间呢？首先我们来看一个Sigmoid函数</p>
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<div class="post">
    <h2 class="post-item post-title">
        <a href="Blog/public/post/01-hello-world/">深度学习Step by Step</a>
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        <span><i class="iconfont icon-today-sharp"></i>&nbsp;2022-10-15</span>

<span><i class="iconfont icon-file-tray-sharp"></i>&nbsp;2320 字</span>
<span><i class="iconfont icon-time-sharp"></i>&nbsp;5 分钟</span>
<span><i class="iconfont icon-pricetags-sharp"></i>&nbsp;<a href="/Blog/public/tags/mindspore">mindspore</a>&nbsp;<a href="/Blog/public/tags/dl">DL</a>&nbsp;</span>

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    <div class="post-item post-summary markdown-body">
        <h1 id="深度学习step-by-step">深度学习Step by Step</h1>
<h2 id="写在前面">写在前面</h2>
<p>本教程是有关深度学习的入门教程，基于一个国产的人工智能引擎，<a href="https://www.mindspore.cn/" target="_blank">mindspore</a>，主要参考了《深度学习》以及《动手学深度学习》，使用MindSpore实现了《动手学深度学习》中很多有用的练习，可以作为深度学习以及MindSpore的入门教程。</p>
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    <h2 class="post-item post-title">
        <a href="Blog/public/post/00-forward_backward_and_mlp/">前向传播、反向传播与感知器</a>
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        <span><i class="iconfont icon-today-sharp"></i>&nbsp;2022-04-10</span>

<span><i class="iconfont icon-file-tray-sharp"></i>&nbsp;725 字</span>
<span><i class="iconfont icon-time-sharp"></i>&nbsp;2 分钟</span>
<span><i class="iconfont icon-pricetags-sharp"></i>&nbsp;<a href="/Blog/public/tags/mindspore">mindspore</a>&nbsp;<a href="/Blog/public/tags/dl">DL</a>&nbsp;</span>

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    <div class="post-item post-summary markdown-body">
        <h2 id="设计思路">设计思路</h2>
<h3 id="前向传播">前向传播</h3>
<p>前向传播是计算输出值$\hat y=W^Tx+b$与损失函数$Loss(\hat y,y)$。具体而言就在神经网络中是根据输入的数据x与网络的权重w经过激活函数后输出$\hat y$，并根据$\hat y$与$y$计算出损失函数的过程。</p>
<h3 id="反向传播">反向传播</h3>
<p>方向传播是计算损失函数的梯度，即$\Delta w=\alpha\frac{\partial L}{\partial w}$，$\Delta b=\alpha\frac{\partial L}{\partial b}$，$w = w - \Delta w $，$b = b - \Delta b $，其中$\alpha$是学习率。根据微积分中的<strong>梯度方向是函数值增长方向</strong>的原理，通过向梯度方向的反方向更新权重与偏置，来使得损失函数的取值最小化的过程。</p>
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    <h2 class="post-item post-title">
        <a href="Blog/public/post/nntoos/">MATLAB神经网络工具箱</a>
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        <span><i class="iconfont icon-today-sharp"></i>&nbsp;0001-01-01</span>

<span><i class="iconfont icon-file-tray-sharp"></i>&nbsp;1114 字</span>
<span><i class="iconfont icon-time-sharp"></i>&nbsp;3 分钟</span>
<span><i class="iconfont icon-pricetags-sharp"></i>&nbsp;<a href="/Blog/public/tags/matlab">MATLAB</a>&nbsp;<a href="/Blog/public/tags/dl">DL</a>&nbsp;</span>

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    <div class="post-item post-summary markdown-body">
        <p>matlab拥有着很多实用的工科计算工具，其中就包含了神经网络工具箱。利用matlab，可以很容易地搭建一个实用的神经网络。</p>
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